Intelligent Adaptive Tracking Control And Stability Of Kinds Of Uncertain Nonlinear Systems

As we all know,most systems are actually nonlinear systems,and usually af-fected by system uncertainties,stochastic disturbances and time delays.Therefore,the research on control and stability of such nonlinear systems has been continuously concerned.In recent years,adaptive control is able to modify the controller parame-ters online based on performance indicators and make the system run in the optimal or suboptimal state,so it has been successfully applied to the control research of nonlinear systems in industrial field.On the other hand,fuzzy logic system and neu-ral network can approximate the unknown nonlinear functions of any system with arbitrary accuracy,so it is an effective way to deal with the uncertainties of nonlin-ear systems.Subsequently,the intelligent adaptive control combined backstepping with neural network or fuzzy logic system has obtained full development and many valuable results.However,there are still many problems to be discussed.First of all,the adaptive fuzzy control problem of several kinds of uncertain nonlinear systems is analyzed in this dissertation,such as for uncertain SISO strict-feedback nonlin-ear system with input dead-zone,for switched nonlinear systems with disturbance,for uncertain MIMO nonlinear system with prescribed constraint.It is self-evident that stability problem is one of the most basic and important problems that people study various dynamic systems,especially industrial systems.Next,for time-delay Markov jump neural networks nonlinear system and time-delay switching positive T-S fuzzy nonlinear systems,the stability of the system is analyzed based on the Lyapunov theory.Finally,the application of intelligent adaptive control in Electric drive control system is studied.The main contents are listed as follows:(1)The problem of fuzzy adaptive tracking control for a class of uncertain strick-feedback nonlinear systems with dead-zone input and unknown bounded dis-turbances is addressed.Dead-zone actuators and unknown disturbances are considered at the same time.The generalized fuzzy hyperbolic model(GFH-M)is used to approximate the unknown nonlinear functions,and the dynamic surface control(DSC)technique is employed to avoid the problem of 'explosion of complexity',and just one adaptive law in the design of controller makes the system more simple and the computational burden lighter.Lyapunov stability analysis shows that all the signals in the closed-loop systems are semi-globally uniformly ultimately bounded(SGUUB).(2)An adaptive fuzzy tracking control is investigated for a class of switched un-certain nonlinear systems in strict-feedback form via the modified backstep-ping technique.The unknown nonlinear functions are approximated by the generalized fuzzy hyperbolic model(GFHM).It is shown that if the designed parameters in the controller and adaptive laws are appropriately selected,then all closed-loop signals are bounded and the stability of the system can be kept under average dwell time methods.In the end,simulation studies are present-ed to illustrate the effectiveness of the proposed method.(3)A constraint control problem is considered for a class of uncertain MIMO strict-feedback nonlinear systems.The controller is designed on the basis of backstepping technology,and a system transformation is carried out by intro-ducing a new constraint variable.By utilizing GFHM to approximate unknown nonlinear function,the adaptive fuzzy controller with prescribed constraint can ensure the transient and steady state performance for the tracking errors of nonlinear system.The Lyapunov stability analysis proves that all signals in the closed-loop system are semi-globally,uniformly and ultimately bounded.Two simulation examples validate the effectiveness of the proposed method.(4)The stability of Markov jump neural networks with interval time-varying dis-tributed delay and uncertain transition rates is investigated.By fully consider-ing the property of transition rates and the characteristic of uncertain domains,a more effective technique in stead of the traditional Young's inequality is used to bind the uncertain terms in the transition rates.By applying the Lyapunov-Krasovskii functional and a less conservative auxiliary function-based integral inequalities,new delay-dependent stability criteria are obtained.The simula-tion results demonstrate the validity of the proposed method.(5)The problem of stability and L1-gain performance is dealt with for a class of continuous-time switched positive T-S fuzzy systems with time-varying delay by implying average dwell time switching signals.Firstly,by implying the mul-tiple linear co-positive Lyapunov-Krasovskii function and average dwell time methods,some sufficient conditions are derived to guarantee the exponential stability of switched positive T-S fuzzy systems with time-varying delay.Then,the weighted L1-gain performance is investigated for the system considered.Finally,simulation examples demonstrate the validity of the main results.(6)For a single link manipulator system driven by DC motor and AC permanent magnet synchronous motor,a fuzzy adaptive controller with unknown distur-bance observer is designed based on backstepping technology,and combined with the generalized fuzzy hyperbolic model which can approximate unknown nonlinear functions in the system.By Lyapunov stability theory,the stabil-ity and convergence of the closed-loop system with unknown disturbance is proved.The proposed control method is verified by simulation.